Problems exist in the field of electronic data benchmarking, where benchmark data estimates may be received from various data sources. For purposes of this disclosure, electronic data benchmarking generally refers to characterizing a large collection of electronic data estimates received over a particular period of time. These benchmark estimates may not be an accurate representation of current data trends in an electronic data exchange environment. Each data source may use its own internal benchmark estimate methodology, which may be different from methodologies of other data sources. For example, an internal methodology may manipulate data values (for example, by emphasizing particular data values and deemphasizing other data values) in order to obtain a favorably-perceived benchmark estimate. In addition, a sender of a benchmark estimate may manipulate its benchmark estimate (compared to other benchmark estimates), to artificially influence a final benchmark value (aggregated across all benchmark estimates). As a result, a downstream computer system that aggregates benchmark estimates may be susceptible to data manipulation and data tampering by the various data sources. Accordingly, there is a need for systems and methods for securing data exchanges and preventing data tampering such that data integrity may be maintained, including for use in electronic data benchmarking functions.